Licensed under the Apache License, Version 2.0 (the "License");
#@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
Welcome to the TensorFlow Hub Object Detection Colab! This notebook will take you through the steps of running an "out-of-the-box" object detection model on images.
Let's start with the base imports.
View on TensorFlow.org
Run in Google Colab
View on GitHub
Download notebook
See TF Hub models# This Colab requires a recent numpy version.
!pip install numpy==1.24.3
!pip install protobuf==3.20.3
Collecting numpy==1.24.3
Downloading numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.3/17.3 MB 52.1 MB/s eta 0:00:00
Installing collected packages: numpy
Attempting uninstall: numpy
Found existing installation: numpy 1.23.5
Uninstalling numpy-1.23.5:
Successfully uninstalled numpy-1.23.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
lida 0.0.10 requires fastapi, which is not installed.
lida 0.0.10 requires kaleido, which is not installed.
lida 0.0.10 requires python-multipart, which is not installed.
lida 0.0.10 requires uvicorn, which is not installed.
numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.3 which is incompatible.
Successfully installed numpy-1.24.3
Requirement already satisfied: protobuf==3.20.3 in /usr/local/lib/python3.10/dist-packages (3.20.3)
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3108, in _dep_map
return self.__dep_map
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 2901, in __getattr__
raise AttributeError(attr)
AttributeError: _DistInfoDistribution__dep_map
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 169, in exc_logging_wrapper
status = run_func(*args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py", line 242, in wrapper
return func(self, options, args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py", line 441, in run
conflicts = self._determine_conflicts(to_install)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py", line 572, in _determine_conflicts
return check_install_conflicts(to_install)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/check.py", line 101, in check_install_conflicts
package_set, _ = create_package_set_from_installed()
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/check.py", line 42, in create_package_set_from_installed
dependencies = list(dist.iter_dependencies())
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/metadata/pkg_resources.py", line 216, in iter_dependencies
return self._dist.requires(extras)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 2821, in requires
dm = self._dep_map
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3110, in _dep_map
self.__dep_map = self._compute_dependencies()
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3120, in _compute_dependencies
reqs.extend(parse_requirements(req))
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py", line 3173, in __init__
super(Requirement, self).__init__(requirement_string)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/requirements.py", line 102, in __init__
req = REQUIREMENT.parseString(requirement_string)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 1131, in parse_string
loc, tokens = self._parse(instring, 0)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 817, in _parseNoCache
loc, tokens = self.parseImpl(instring, pre_loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 3886, in parseImpl
loc, exprtokens = e._parse(instring, loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 817, in _parseNoCache
loc, tokens = self.parseImpl(instring, pre_loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 4114, in parseImpl
return e._parse(
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 817, in _parseNoCache
loc, tokens = self.parseImpl(instring, pre_loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 3886, in parseImpl
loc, exprtokens = e._parse(instring, loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 817, in _parseNoCache
loc, tokens = self.parseImpl(instring, pre_loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 4959, in parseImpl
loc, tokens = self_expr._parse(instring, loc, doActions, callPreParse=False)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 817, in _parseNoCache
loc, tokens = self.parseImpl(instring, pre_loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 3886, in parseImpl
loc, exprtokens = e._parse(instring, loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 856, in _parseNoCache
tokens = fn(instring, tokens_start, ret_tokens)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 291, in wrapper
ret = func(*args[limit:])
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/requirements.py", line 71, in <lambda>
lambda s, l, t: Marker(s[t._original_start : t._original_end])
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/markers.py", line 278, in __init__
self._markers = _coerce_parse_result(MARKER.parseString(marker))
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 1131, in parse_string
loc, tokens = self._parse(instring, 0)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 817, in _parseNoCache
loc, tokens = self.parseImpl(instring, pre_loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 3886, in parseImpl
loc, exprtokens = e._parse(instring, loc, doActions)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py", line 781, in _parseNoCache
len_instring = len(instring)
KeyboardInterrupt
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/bin/pip3", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py", line 79, in main
return command.main(cmd_args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 101, in main
return self._main(args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 223, in _main
return run(options, args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 206, in exc_logging_wrapper
logger.critical("Operation cancelled by user")
File "/usr/lib/python3.10/logging/__init__.py", line 1524, in critical
self._log(CRITICAL, msg, args, **kwargs)
File "/usr/lib/python3.10/logging/__init__.py", line 1624, in _log
self.handle(record)
File "/usr/lib/python3.10/logging/__init__.py", line 1634, in handle
self.callHandlers(record)
File "/usr/lib/python3.10/logging/__init__.py", line 1696, in callHandlers
hdlr.handle(record)
File "/usr/lib/python3.10/logging/__init__.py", line 968, in handle
self.emit(record)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/utils/logging.py", line 169, in emit
renderable = self.render_message(record, message)
File "/usr/local/lib/python3.10/dist-packages/pip/_vendor/rich/logging.py", line 174, in render_message
def render_message(self, record: LogRecord, message: str) -> "ConsoleRenderable":
KeyboardInterrupt
^C
import os
import pathlib
import matplotlib
import matplotlib.pyplot as plt
import io
import scipy.misc
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
from six.moves.urllib.request import urlopen
import tensorflow as tf
import tensorflow_hub as hub
tf.get_logger().setLevel('ERROR')
Run the following cell to create some utils that will be needed later:
# @title Run this!!
def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.
Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.
Args:
path: the file path to the image
Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
image = None
if(path.startswith('http')):
response = urlopen(path)
image_data = response.read()
image_data = BytesIO(image_data)
image = Image.open(image_data)
else:
image_data = tf.io.gfile.GFile(path, 'rb').read()
image = Image.open(BytesIO(image_data))
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(1, im_height, im_width, 3)).astype(np.uint8)
ALL_MODELS = {
'CenterNet HourGlass104 512x512' : 'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1',
'CenterNet HourGlass104 Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1',
'CenterNet HourGlass104 1024x1024' : 'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024/1',
'CenterNet HourGlass104 Keypoints 1024x1024' : 'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024_kpts/1',
'CenterNet Resnet50 V1 FPN 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1',
'CenterNet Resnet50 V1 FPN Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512_kpts/1',
'CenterNet Resnet101 V1 FPN 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet101v1_fpn_512x512/1',
'CenterNet Resnet50 V2 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512/1',
'CenterNet Resnet50 V2 Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512_kpts/1',
'EfficientDet D0 512x512' : 'https://tfhub.dev/tensorflow/efficientdet/d0/1',
'EfficientDet D1 640x640' : 'https://tfhub.dev/tensorflow/efficientdet/d1/1',
'EfficientDet D2 768x768' : 'https://tfhub.dev/tensorflow/efficientdet/d2/1',
'EfficientDet D3 896x896' : 'https://tfhub.dev/tensorflow/efficientdet/d3/1',
'EfficientDet D4 1024x1024' : 'https://tfhub.dev/tensorflow/efficientdet/d4/1',
'EfficientDet D5 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d5/1',
'EfficientDet D6 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d6/1',
'EfficientDet D7 1536x1536' : 'https://tfhub.dev/tensorflow/efficientdet/d7/1',
'SSD MobileNet v2 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2',
'SSD MobileNet V1 FPN 640x640' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v1/fpn_640x640/1',
'SSD MobileNet V2 FPNLite 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1',
'SSD MobileNet V2 FPNLite 640x640' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1',
'SSD ResNet50 V1 FPN 640x640 (RetinaNet50)' : 'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_640x640/1',
'SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)' : 'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_1024x1024/1',
'SSD ResNet101 V1 FPN 640x640 (RetinaNet101)' : 'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_640x640/1',
'SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)' : 'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_1024x1024/1',
'SSD ResNet152 V1 FPN 640x640 (RetinaNet152)' : 'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_640x640/1',
'SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)' : 'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_1024x1024/1',
'Faster R-CNN ResNet50 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1',
'Faster R-CNN ResNet50 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_1024x1024/1',
'Faster R-CNN ResNet50 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_800x1333/1',
'Faster R-CNN ResNet101 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_640x640/1',
'Faster R-CNN ResNet101 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_1024x1024/1',
'Faster R-CNN ResNet101 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_800x1333/1',
'Faster R-CNN ResNet152 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_640x640/1',
'Faster R-CNN ResNet152 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_1024x1024/1',
'Faster R-CNN ResNet152 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_800x1333/1',
'Faster R-CNN Inception ResNet V2 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_640x640/1',
'Faster R-CNN Inception ResNet V2 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_1024x1024/1',
'Mask R-CNN Inception ResNet V2 1024x1024' : 'https://tfhub.dev/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1'
}
IMAGES_FOR_TEST = {
'Beach' : 'models/research/object_detection/test_images/image2.jpg',
'Dogs' : 'models/research/object_detection/test_images/image1.jpg',
# By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg
'Naxos Taverna' : 'https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg',
# Source: https://commons.wikimedia.org/wiki/File:The_Coleoptera_of_the_British_islands_(Plate_125)_(8592917784).jpg
'Beatles' : 'https://upload.wikimedia.org/wikipedia/commons/1/1b/The_Coleoptera_of_the_British_islands_%28Plate_125%29_%288592917784%29.jpg',
# By Américo Toledano, Source: https://commons.wikimedia.org/wiki/File:Biblioteca_Maim%C3%B3nides,_Campus_Universitario_de_Rabanales_007.jpg
'Phones' : 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg/1024px-Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg',
# Source: https://commons.wikimedia.org/wiki/File:The_smaller_British_birds_(8053836633).jpg
'Birds' : 'https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_%288053836633%29.jpg',
'Zevent' : 'custom/zevent.jpg',
'Bouchon' : 'custom/bouchon.jpg',
'Bouchon mais chiant' : 'custom/fourviere.jpg',
'Bouchon mais pire' : 'custom/bouchon2.jpg',
'Savane' : 'custom/zebra.jpg',
'Elevage' : 'custom/elevage.jpg',
'Lycee' : 'custom/lycee.jpg'
}
COCO17_HUMAN_POSE_KEYPOINTS = [(0, 1),
(0, 2),
(1, 3),
(2, 4),
(0, 5),
(0, 6),
(5, 7),
(7, 9),
(6, 8),
(8, 10),
(5, 6),
(5, 11),
(6, 12),
(11, 12),
(11, 13),
(13, 15),
(12, 14),
(14, 16)]
To visualize the images with the proper detected boxes, keypoints and segmentation, we will use the TensorFlow Object Detection API. To install it we will clone the repo.
# Clone the tensorflow models repository
!git clone --depth 1 https://github.com/tensorflow/models
Cloning into 'models'... remote: Enumerating objects: 3996, done. remote: Counting objects: 100% (3996/3996), done. remote: Compressing objects: 100% (3029/3029), done. remote: Total 3996 (delta 1176), reused 2743 (delta 910), pack-reused 0 Receiving objects: 100% (3996/3996), 53.29 MiB | 25.04 MiB/s, done. Resolving deltas: 100% (1176/1176), done.
Intalling the Object Detection API
%%bash
sudo apt install -y protobuf-compiler
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .
Reading package lists...
Building dependency tree...
Reading state information...
protobuf-compiler is already the newest version (3.12.4-1ubuntu7.22.04.1).
0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.
Processing /content/models/research
Preparing metadata (setup.py): started
Preparing metadata (setup.py): finished with status 'done'
Collecting avro-python3 (from object-detection==0.1)
Downloading avro-python3-1.10.2.tar.gz (38 kB)
Preparing metadata (setup.py): started
Preparing metadata (setup.py): finished with status 'done'
Collecting apache-beam (from object-detection==0.1)
Downloading apache_beam-2.51.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.7 MB)
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Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from object-detection==0.1) (9.4.0)
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Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from object-detection==0.1) (1.16.0)
Requirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (from object-detection==0.1) (2.0.7)
Collecting lvis (from object-detection==0.1)
Downloading lvis-0.5.3-py3-none-any.whl (14 kB)
Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from object-detection==0.1) (1.11.3)
Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from object-detection==0.1) (1.5.3)
Collecting tf-models-official>=2.5.1 (from object-detection==0.1)
Downloading tf_models_official-2.14.2-py2.py3-none-any.whl (2.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 46.7 MB/s eta 0:00:00
Collecting tensorflow_io (from object-detection==0.1)
Downloading tensorflow_io-0.34.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (28.8 MB)
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Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from object-detection==0.1) (2.14.0)
Collecting pyparsing==2.4.7 (from object-detection==0.1)
Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
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Collecting sacrebleu<=2.2.0 (from object-detection==0.1)
Downloading sacrebleu-2.2.0-py3-none-any.whl (116 kB)
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Collecting portalocker (from sacrebleu<=2.2.0->object-detection==0.1)
Downloading portalocker-2.8.2-py3-none-any.whl (17 kB)
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Collecting colorama (from sacrebleu<=2.2.0->object-detection==0.1)
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Building wheels for collected packages: object-detection, avro-python3, crcmod, dill, hdfs, seqeval, pyjsparser, docopt
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WARNING: apt does not have a stable CLI interface. Use with caution in scripts.
Now we can import the dependencies we will need later
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import ops as utils_ops
%matplotlib inline
Label maps correspond index numbers to category names, so that when our convolution network predicts 5, we know that this corresponds to airplane. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine.
We are going, for simplicity, to load from the repository that we loaded the Object Detection API code
PATH_TO_LABELS = './models/research/object_detection/data/mscoco_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
Here we will choose which Object Detection model we will use. Select the architecture and it will be loaded automatically. If you want to change the model to try other architectures later, just change the next cell and execute following ones.
Tip: if you want to read more details about the selected model, you can follow the link (model handle) and read additional documentation on TF Hub. After you select a model, we will print the handle to make it easier.
#@title Model Selection { display-mode: "form", run: "auto" }
model_display_name = 'SSD MobileNet V1 FPN 640x640' # @param ['CenterNet HourGlass104 512x512','CenterNet HourGlass104 Keypoints 512x512','CenterNet HourGlass104 1024x1024','CenterNet HourGlass104 Keypoints 1024x1024','CenterNet Resnet50 V1 FPN 512x512','CenterNet Resnet50 V1 FPN Keypoints 512x512','CenterNet Resnet101 V1 FPN 512x512','CenterNet Resnet50 V2 512x512','CenterNet Resnet50 V2 Keypoints 512x512','EfficientDet D0 512x512','EfficientDet D1 640x640','EfficientDet D2 768x768','EfficientDet D3 896x896','EfficientDet D4 1024x1024','EfficientDet D5 1280x1280','EfficientDet D6 1280x1280','EfficientDet D7 1536x1536','SSD MobileNet v2 320x320','SSD MobileNet V1 FPN 640x640','SSD MobileNet V2 FPNLite 320x320','SSD MobileNet V2 FPNLite 640x640','SSD ResNet50 V1 FPN 640x640 (RetinaNet50)','SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)','SSD ResNet101 V1 FPN 640x640 (RetinaNet101)','SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)','SSD ResNet152 V1 FPN 640x640 (RetinaNet152)','SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)','Faster R-CNN ResNet50 V1 640x640','Faster R-CNN ResNet50 V1 1024x1024','Faster R-CNN ResNet50 V1 800x1333','Faster R-CNN ResNet101 V1 640x640','Faster R-CNN ResNet101 V1 1024x1024','Faster R-CNN ResNet101 V1 800x1333','Faster R-CNN ResNet152 V1 640x640','Faster R-CNN ResNet152 V1 1024x1024','Faster R-CNN ResNet152 V1 800x1333','Faster R-CNN Inception ResNet V2 640x640','Faster R-CNN Inception ResNet V2 1024x1024','Mask R-CNN Inception ResNet V2 1024x1024']
model_handle = ALL_MODELS[model_display_name]
print('Selected model:'+ model_display_name)
print('Model Handle at TensorFlow Hub: {}'.format(model_handle))
Selected model:SSD MobileNet V1 FPN 640x640 Model Handle at TensorFlow Hub: https://tfhub.dev/tensorflow/ssd_mobilenet_v1/fpn_640x640/1
Here we just need the model handle that was selected and use the Tensorflow Hub library to load it to memory.
print('loading model...')
hub_model = hub.load(model_handle)
print('model loaded!')
loading model... model loaded!
Let's try the model on a simple image. To help with this, we provide a list of test images.
Here are some simple things to try out if you are curious:
Be careful: when using images with an alpha channel, the model expect 3 channels images and the alpha will count as a 4th.
#@title Image Selection (don't forget to execute the cell!) { display-mode: "form"}
selected_image = 'Lycee' # @param ['Beach', 'Savane', 'Zevent', 'Bouchon', 'Bouchon mais chiant', 'Bouchon mais pire', 'Elevage', 'Lycee' ]
flip_image_horizontally = False #@param {type:"boolean"}
convert_image_to_grayscale = False #@param {type:"boolean"}
image_path = IMAGES_FOR_TEST[selected_image]
image_np = load_image_into_numpy_array(image_path)
# Flip horizontally
if(flip_image_horizontally):
image_np[0] = np.fliplr(image_np[0]).copy()
# Convert image to grayscale
if(convert_image_to_grayscale):
image_np[0] = np.tile(
np.mean(image_np[0], 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
plt.figure(figsize=(24,32))
plt.imshow(image_np[0])
plt.show()
To do the inference we just need to call our TF Hub loaded model.
Things you can try:
result['detection_boxes'] and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).# running inference
results = hub_model(image_np)
# different object detection models have additional results
# all of them are explained in the documentation
result = {key:value.numpy() for key,value in results.items()}
print(result.keys())
dict_keys(['detection_anchor_indices', 'raw_detection_scores', 'num_detections', 'detection_classes', 'raw_detection_boxes', 'detection_multiclass_scores', 'detection_boxes', 'detection_scores'])
Here is where we will need the TensorFlow Object Detection API to show the squares from the inference step (and the keypoints when available).
the full documentation of this method can be seen here
Here you can, for example, set min_score_thresh to other values (between 0 and 1) to allow more detections in or to filter out more detections.
label_id_offset = 0
image_np_with_detections = image_np.copy()
# Use keypoints if available in detections
keypoints, keypoint_scores = None, None
if 'detection_keypoints' in result:
keypoints = result['detection_keypoints'][0]
keypoint_scores = result['detection_keypoint_scores'][0]
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections[0],
result['detection_boxes'][0],
(result['detection_classes'][0] + label_id_offset).astype(int),
result['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False,
keypoints=keypoints,
keypoint_scores=keypoint_scores,
keypoint_edges=COCO17_HUMAN_POSE_KEYPOINTS)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_detections[0])
plt.show()
Among the available object detection models there's Mask R-CNN and the output of this model allows instance segmentation.
To visualize it we will use the same method we did before but adding an aditional parameter: instance_masks=output_dict.get('detection_masks_reframed', None)
# Handle models with masks:
image_np_with_mask = image_np.copy()
if 'detection_masks' in result:
# we need to convert np.arrays to tensors
detection_masks = tf.convert_to_tensor(result['detection_masks'][0])
detection_boxes = tf.convert_to_tensor(result['detection_boxes'][0])
# Reframe the bbox mask to the image size.
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes,
image_np.shape[1], image_np.shape[2])
detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
tf.uint8)
result['detection_masks_reframed'] = detection_masks_reframed.numpy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_mask[0],
result['detection_boxes'][0],
(result['detection_classes'][0] + label_id_offset).astype(int),
result['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False,
instance_masks=result.get('detection_masks_reframed', None),
line_thickness=8)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_mask[0])
plt.show()
